{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "2d3412dc",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\owner\\anaconda3\\lib\\site-packages\\scipy\\__init__.py:138: UserWarning: A NumPy version >=1.16.5 and <1.23.0 is required for this version of SciPy (detected version 1.23.3)\n",
      "  warnings.warn(f\"A NumPy version >={np_minversion} and <{np_maxversion} is required for this version of \"\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib as mpl\n",
    "import datetime as timedelta\n",
    "import matplotlib.mlab as ml\n",
    "from mpl_toolkits.mplot3d import Axes3D\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "from sklearn.linear_model import LinearRegression\n",
    "import statsmodels.api as sm\n",
    "from statsmodels.formula.api import ols"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "2e2d4875",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "\n",
    "def concat(*arrs) -> np.ndarray:\n",
    "    return np.concatenate(tuple(map(np.asarray, arrs)))\n",
    "\n",
    "def insert_at(outer_arr, arr, n) -> np.ndarray:\n",
    "    outer_arr = np.asarray(outer_arr)\n",
    "    prev, post = np.split(outer_arr, (n,))\n",
    "    return concat(prev, arr, post)\n",
    "\n",
    "def cross2d(x1, y1, x2, y2):\n",
    "    return x1*y2-x2*y1\n",
    "\n",
    "def is_clockwise(x1, y1, x2, y2):\n",
    "    cp = cross2d(x1, y1, x2, y2)\n",
    "    return cp < 0 if cp != 0 else None\n",
    "\n",
    "def fill_outside(x, y, ll, ur, counter_clockwise=None):\n",
    "    \"\"\"\n",
    "    Creates a polygon where x and y form a crevice of an outer\n",
    "    rectangle with lower left and upper right corners `ll` and `ur`\n",
    "    respectively. If `counter_clockwise` is `None` then the orientation\n",
    "    of the outer polygon will be guessed to be the opposite of the\n",
    "    inner connecting points.\n",
    "    \"\"\"\n",
    "    x = np.asarray(x)\n",
    "    y = np.asarray(y)\n",
    "    xmin, ymin = ll\n",
    "    xmax, ymax = ur\n",
    "    xmin, ymin = min(xmin, min(x)), min(ymin, min(y))\n",
    "    xmax, ymax = max(xmax, max(x)), max(ymax, max(y))\n",
    "    corners = np.array([\n",
    "        [xmin, ymin],\n",
    "        [xmin, ymax],\n",
    "        [xmax, ymax],\n",
    "        [xmax, ymin],\n",
    "        [xmin, ymin],\n",
    "    ])\n",
    "    lower_left = corners[0]\n",
    "    # Get closest point to splicing corner\n",
    "    x_off, y_off = x-lower_left[0], y-lower_left[1]\n",
    "    closest_n = (x_off**2+y_off**2).argmin()\n",
    "    # Guess orientation\n",
    "    p = [x_off[closest_n], y_off[closest_n]]\n",
    "    try:\n",
    "        pn = [x_off[closest_n+1], y_off[closest_n+1]]\n",
    "    except IndexError:\n",
    "        # wrap around if we're at the end of the array\n",
    "        pn = [x_off[0], y_off[0]]\n",
    "    if counter_clockwise is None:\n",
    "        counter_clockwise = not is_clockwise(*p, *pn)\n",
    "    corners = corners[::-1] if counter_clockwise else corners\n",
    "    # Join the arrays\n",
    "    corners = concat(np.array([[x[closest_n], y[closest_n]]]), corners)\n",
    "    xs, ys = np.transpose(corners)\n",
    "    return insert_at(x, xs, closest_n), insert_at(y, ys, closest_n)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "d96750bd",
   "metadata": {},
   "outputs": [],
   "source": [
    "DJR= pd.read_csv('DJR.csv')\n",
    "df1_D = pd.read_csv(\"df1_D.csv\")\n",
    "df2_D = pd.read_csv(\"df2_D.csv\")\n",
    "df3_D = pd.read_csv(\"df3_D.csv\")\n",
    "df4_D = pd.read_csv(\"df4_D.csv\")\n",
    "df5_D = pd.read_csv(\"df5_D.csv\")\n",
    "df6_D = pd.read_csv(\"df6_D.csv\")\n",
    "df7_D = pd.read_csv(\"df7_D.csv\")\n",
    "df8_D = pd.read_csv(\"df8_D.csv\")\n",
    "df9_D = pd.read_csv(\"df9_D.csv\")\n",
    "df10_D = pd.read_csv(\"df10_D.csv\")\n",
    "df11_D = pd.read_csv(\"df11_D.csv\")\n",
    "df12_D = pd.read_csv(\"df12_D.csv\")\n",
    "df13_D = pd.read_csv(\"df13_D.csv\")\n",
    "df14_D = pd.read_csv(\"df14_D.csv\")\n",
    "df15_D = pd.read_csv(\"df15_D.csv\")\n",
    "df16_D = pd.read_csv(\"df16_D.csv\")\n",
    "df17_D = pd.read_csv(\"df17_D.csv\")\n",
    "df18_D = pd.read_csv(\"df18_D.csv\")\n",
    "df19_D = pd.read_csv(\"df19_D.csv\")\n",
    "df20_D = pd.read_csv(\"df20_D.csv\")\n",
    "df21_D = pd.read_csv(\"df21_D.csv\")\n",
    "df22_D = pd.read_csv(\"df22_D.csv\")\n",
    "df23_D = pd.read_csv(\"df23_D.csv\")\n",
    "df24_D = pd.read_csv(\"df24_D.csv\")\n",
    "df25_D = pd.read_csv(\"df25_D.csv\")\n",
    "df26_D = pd.read_csv(\"df26_D.csv\")\n",
    "df27_D = pd.read_csv(\"df27_D.csv\")\n",
    "df28_D = pd.read_csv(\"df28_D.csv\")\n",
    "df29_D = pd.read_csv(\"df29_D.csv\")\n",
    "df30_D = pd.read_csv(\"df30_D.csv\")\n",
    "df31_D = pd.read_csv(\"df31_D.csv\")\n",
    "df32_D = pd.read_csv(\"df32_D.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "0903687f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 388.8x748.8 with 32 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(5.4,10.4))\n",
    "\n",
    "ll, ur = (129.291, 36.026), (129.2955, 36.032)\n",
    "plt.rc('font', size = 10)\n",
    "plt.rc('axes', labelsize = 10)\n",
    "plt.rc('xtick', labelsize = 10)\n",
    "plt.rc('ytick', labelsize = 10)\n",
    "plt.rc('legend', fontsize = 10)\n",
    "plt.rc('figure', titlesize = 10)\n",
    "\n",
    "\n",
    "plt.subplot(8,4,1)\n",
    "\n",
    "x, y = fill_outside(DJR[\"LON\"], DJR[\"LAT\"], ll, ur)\n",
    "\n",
    "plt.fill(x,y, color = 'gray')\n",
    "plt.plot(DJR[\"LON\"], DJR[\"LAT\"], \n",
    "         color='black', linewidth = 1)\n",
    "plt.scatter(df1_D[\"LON\"], df1_D[\"LAT\"], c=df1_D[\"Depth\"], cmap='rainbow_r',vmin = 0, vmax =9, alpha=1, s=2, marker = 's')\n",
    "\n",
    "plt.xticks([129.292, 129.294], \n",
    "           labels = ['',''])\n",
    "\n",
    "plt.xlim([129.291, 129.2955])\n",
    "plt.yticks([36.027, 36.029, 36.031], labels = ['','',''])\n",
    "plt.ylim([36.026, 36.032])\n",
    "\n",
    "plt.title(\"\")\n",
    "plt.xlabel(\"\")\n",
    "plt.ylabel('')\n",
    "plt.tick_params(length = 10) \n",
    "plt.grid(alpha=0.5, color='gray')\n",
    "\n",
    "\n",
    "plt.subplot(8,4,2)\n",
    "plt.fill(x,y, color = 'gray')\n",
    "plt.plot(DJR[\"LON\"], DJR[\"LAT\"], \n",
    "         color='black', linewidth = 1)\n",
    "plt.scatter(df2_D[\"LON\"], df2_D[\"LAT\"], c=df2_D[\"Depth\"], cmap='rainbow_r',vmin = 0, vmax =9, alpha=1, s=2, marker = 's')\n",
    "\n",
    "plt.xticks([129.292, 129.294], \n",
    "           labels = ['',''])\n",
    "plt.yticks([36.027, 36.029, 36.031], labels = ['','',''])\n",
    "plt.xlim([129.291, 129.2955])\n",
    "plt.ylim([36.026, 36.032])\n",
    "\n",
    "\n",
    "plt.title(\"\")\n",
    "plt.xlabel(\"\")\n",
    "plt.ylabel('')\n",
    "plt.tick_params(length = 10) \n",
    "plt.grid(alpha=0.5, color='gray')\n",
    "\n",
    "\n",
    "plt.subplot(8,4,3)\n",
    "plt.fill(x,y, color = 'gray')\n",
    "plt.plot(DJR[\"LON\"], DJR[\"LAT\"], \n",
    "         color='black', linewidth = 1)\n",
    "plt.scatter(df3_D[\"LON\"], df3_D[\"LAT\"], c=df3_D[\"Depth\"], cmap='rainbow_r',vmin = 0, vmax =9, alpha=1, s=2, marker = 's')\n",
    "\n",
    "plt.xticks([129.292, 129.294], \n",
    "           labels = ['',''])\n",
    "plt.yticks([36.027, 36.029, 36.031], labels = ['','',''])\n",
    "plt.xlim([129.291, 129.2955])\n",
    "plt.ylim([36.026, 36.032])\n",
    "\n",
    "plt.title(\"\")\n",
    "plt.xlabel(\"\")\n",
    "plt.ylabel('')\n",
    "plt.tick_params(length = 10) \n",
    "plt.grid(alpha=0.5, color='gray')\n",
    "\n",
    "\n",
    "plt.subplot(8,4,4)\n",
    "plt.fill(x,y, color = 'gray')\n",
    "plt.plot(DJR[\"LON\"], DJR[\"LAT\"], \n",
    "         color='black', linewidth = 1)\n",
    "plt.scatter(df4_D[\"LON\"], df4_D[\"LAT\"], c=df4_D[\"Depth\"], cmap='rainbow_r',vmin = 0, vmax =9, alpha=1, s=2, marker = 's')\n",
    "\n",
    "plt.xticks([129.292, 129.294], \n",
    "           labels = ['',''])\n",
    "plt.yticks([36.027, 36.029, 36.031], labels = ['','',''])\n",
    "plt.xlim([129.291, 129.2955])\n",
    "plt.ylim([36.026, 36.032])\n",
    "\n",
    "plt.title(\"\")\n",
    "plt.xlabel(\"\")\n",
    "plt.ylabel('')\n",
    "\n",
    "plt.tick_params(length = 10) \n",
    "plt.grid(alpha=0.5, color='gray')\n",
    "\n",
    "plt.subplot(8,4,5)\n",
    "plt.fill(x,y, color = 'gray')\n",
    "plt.plot(DJR[\"LON\"], DJR[\"LAT\"], \n",
    "         color='black', linewidth = 1)\n",
    "plt.scatter(df5_D[\"LON\"], df5_D[\"LAT\"], c=df5_D[\"Depth\"], cmap='rainbow_r',vmin = 0, vmax =9, alpha=1, s=2, marker = 's')\n",
    "\n",
    "plt.xticks([129.292, 129.294], \n",
    "           labels = ['',''])\n",
    "\n",
    "plt.xlim([129.291, 129.2955])\n",
    "plt.yticks([36.027, 36.029, 36.031], labels = ['','',''])\n",
    "plt.ylim([36.026, 36.032])\n",
    "\n",
    "plt.title(\"\")\n",
    "plt.xlabel(\"\")\n",
    "plt.ylabel('')\n",
    "\n",
    "plt.tick_params(length = 10) \n",
    "plt.grid(alpha=0.5, color='gray')\n",
    "\n",
    "plt.subplot(8,4,6)\n",
    "plt.fill(x,y, color = 'gray')\n",
    "plt.plot(DJR[\"LON\"], DJR[\"LAT\"], \n",
    "         color='black', linewidth = 1)\n",
    "plt.scatter(df6_D[\"LON\"], df6_D[\"LAT\"], c=df6_D[\"Depth\"], cmap='rainbow_r',\n",
    "            vmin = 0, vmax =9, alpha=1, s=2, \n",
    "            marker = 's')\n",
    "\n",
    "plt.xticks([129.292, 129.294], \n",
    "           labels = ['',''])\n",
    "plt.yticks([36.027, 36.029, 36.031], labels = ['','',''])\n",
    "plt.xlim([129.291, 129.2955])\n",
    "plt.ylim([36.026, 36.032])\n",
    "\n",
    "plt.title(\"\")\n",
    "plt.xlabel(\"\")\n",
    "plt.ylabel('')\n",
    "\n",
    "plt.tick_params(length = 10) \n",
    "plt.grid(alpha=0.5, color='gray')\n",
    "\n",
    "plt.subplot(8,4,7)\n",
    "plt.fill(x,y, color = 'gray')\n",
    "plt.plot(DJR[\"LON\"], DJR[\"LAT\"], \n",
    "         color='black', linewidth = 1)\n",
    "plt.scatter(df7_D[\"LON\"], df7_D[\"LAT\"], c=df7_D[\"Depth\"], cmap='rainbow_r',\n",
    "            vmin = 0, vmax =9, alpha=1, s=2, \n",
    "            marker = 's')\n",
    "\n",
    "plt.xticks([129.292, 129.294], \n",
    "           labels = ['',''])\n",
    "plt.yticks([36.027, 36.029, 36.031], labels = ['','',''])\n",
    "plt.xlim([129.291, 129.2955])\n",
    "plt.ylim([36.026, 36.032])\n",
    "\n",
    "plt.title(\"\")\n",
    "plt.xlabel(\"\")\n",
    "plt.ylabel('')\n",
    "\n",
    "plt.tick_params(length = 10) \n",
    "plt.grid(alpha=0.5, color='gray')\n",
    "\n",
    "plt.subplot(8,4,8)\n",
    "plt.fill(x,y, color = 'gray')\n",
    "plt.plot(DJR[\"LON\"], DJR[\"LAT\"], \n",
    "         color='black', linewidth = 1)\n",
    "plt.scatter(df8_D[\"LON\"], df8_D[\"LAT\"], c=df8_D[\"Depth\"], cmap='rainbow_r',\n",
    "            vmin = 0, vmax =9, alpha=1, s=2, \n",
    "            marker = 's')\n",
    "\n",
    "plt.xticks([129.292, 129.294], \n",
    "           labels = ['',''])\n",
    "plt.yticks([36.027, 36.029, 36.031], labels = ['','',''])\n",
    "plt.xlim([129.291, 129.2955])\n",
    "plt.ylim([36.026, 36.032])\n",
    "\n",
    "plt.title(\"\")\n",
    "plt.xlabel(\"\")\n",
    "plt.ylabel('')\n",
    "plt.tick_params(length = 10) \n",
    "plt.grid(alpha=0.5, color='gray')\n",
    "\n",
    "\n",
    "plt.subplot(8,4,9)\n",
    "plt.fill(x,y, color = 'gray')\n",
    "plt.plot(DJR[\"LON\"], DJR[\"LAT\"], \n",
    "         color='black', linewidth = 1)\n",
    "plt.scatter(df9_D[\"LON\"], df9_D[\"LAT\"], c=df9_D[\"Depth\"], cmap='rainbow_r',\n",
    "            vmin = 0, vmax =9, alpha=1, s=2, \n",
    "            marker = 's')\n",
    "\n",
    "plt.xticks([129.292, 129.294], \n",
    "           labels = ['',''])\n",
    "\n",
    "plt.xlim([129.291, 129.2955])\n",
    "plt.yticks([36.027, 36.029, 36.031], labels = ['','',''])\n",
    "plt.ylim([36.026, 36.032])\n",
    "\n",
    "\n",
    "plt.title(\"\")\n",
    "plt.xlabel(\"\")\n",
    "plt.ylabel('')\n",
    "\n",
    "plt.tick_params(length = 10) \n",
    "plt.grid(alpha=0.5, color='gray')\n",
    "\n",
    "\n",
    "plt.subplot(8,4,10)\n",
    "plt.fill(x,y, color = 'gray')\n",
    "plt.plot(DJR[\"LON\"], DJR[\"LAT\"], \n",
    "         color='black', linewidth = 1)\n",
    "plt.scatter(df10_D[\"LON\"], df10_D[\"LAT\"], c=df10_D[\"Depth\"], cmap='rainbow_r',\n",
    "            vmin = 0, vmax =9, alpha=1, s=2, \n",
    "            marker = 's')\n",
    "\n",
    "plt.xticks([129.292, 129.294], \n",
    "           labels = ['',''])\n",
    "plt.yticks([36.027, 36.029, 36.031], labels = ['','',''])\n",
    "plt.xlim([129.291, 129.2955])\n",
    "plt.ylim([36.026, 36.032])\n",
    "\n",
    "plt.title(\"\")\n",
    "plt.xlabel(\"\")\n",
    "plt.ylabel('')\n",
    "plt.tick_params(length = 10) \n",
    "plt.grid(alpha=0.5, color='gray')\n",
    "\n",
    "\n",
    "\n",
    "plt.subplot(8,4,11)\n",
    "plt.fill(x,y, color = 'gray')\n",
    "plt.plot(DJR[\"LON\"], DJR[\"LAT\"], \n",
    "         color='black', linewidth = 1)\n",
    "plt.scatter(df11_D[\"LON\"], df11_D[\"LAT\"], c=df11_D[\"Depth\"], cmap='rainbow_r',\n",
    "            vmin = 0, vmax =9, alpha=1, s=2, \n",
    "            marker = 's')\n",
    "\n",
    "plt.xticks([129.292, 129.294], \n",
    "           labels = ['',''])\n",
    "plt.yticks([36.027, 36.029, 36.031], labels = ['','',''])\n",
    "plt.xlim([129.291, 129.2955])\n",
    "plt.ylim([36.026, 36.032])\n",
    "\n",
    "\n",
    "plt.title(\"\")\n",
    "plt.xlabel(\"\")\n",
    "plt.ylabel('')\n",
    "\n",
    "plt.tick_params(length = 10) \n",
    "plt.grid(alpha=0.5, color='gray')\n",
    "\n",
    "plt.subplot(8,4,12)\n",
    "plt.fill(x,y, color = 'gray')\n",
    "plt.plot(DJR[\"LON\"], DJR[\"LAT\"], \n",
    "         color='black', linewidth = 1)\n",
    "plt.scatter(df12_D[\"LON\"], df12_D[\"LAT\"], c=df12_D[\"Depth\"], cmap='rainbow_r',\n",
    "            vmin = 0, vmax =9, alpha=1, s=2, \n",
    "            marker = 's')\n",
    "\n",
    "plt.xticks([129.292, 129.294], \n",
    "           labels = ['',''])\n",
    "plt.yticks([36.027, 36.029, 36.031], labels = ['','',''])\n",
    "plt.xlim([129.291, 129.2955])\n",
    "plt.ylim([36.026, 36.032])\n",
    "\n",
    "plt.title(\"\")\n",
    "plt.xlabel(\"\")\n",
    "plt.ylabel('')\n",
    "\n",
    "plt.tick_params(length = 10) \n",
    "plt.grid(alpha=0.5, color='gray')\n",
    "\n",
    "plt.subplot(8,4,13)\n",
    "plt.fill(x,y, color = 'gray')\n",
    "plt.plot(DJR[\"LON\"], DJR[\"LAT\"], \n",
    "         color='black', linewidth = 1)\n",
    "plt.scatter(df13_D[\"LON\"], df13_D[\"LAT\"], c=df13_D[\"Depth\"], cmap='rainbow_r',\n",
    "            vmin = 0, vmax =9, alpha=1, s=2, \n",
    "            marker = 's')\n",
    "\n",
    "plt.xticks([129.292, 129.294], \n",
    "           labels = ['',''])\n",
    "\n",
    "plt.xlim([129.291, 129.2955])\n",
    "plt.yticks([36.027, 36.029, 36.031], labels = ['','',''])\n",
    "plt.ylim([36.026, 36.032])\n",
    "\n",
    "plt.title(\"\")\n",
    "plt.xlabel(\"\")\n",
    "plt.ylabel('')\n",
    "\n",
    "plt.tick_params(length = 10) \n",
    "plt.grid(alpha=0.5, color='gray')\n",
    "\n",
    "plt.subplot(8,4,14)\n",
    "plt.fill(x,y, color = 'gray')\n",
    "plt.plot(DJR[\"LON\"], DJR[\"LAT\"], \n",
    "         color='black', linewidth = 1)\n",
    "plt.scatter(df14_D[\"LON\"], df14_D[\"LAT\"], c=df14_D[\"Depth\"], cmap='rainbow_r',\n",
    "            vmin = 0, vmax =9, alpha=1, s=2, \n",
    "            marker = 's')\n",
    "\n",
    "plt.xticks([129.292, 129.294], \n",
    "           labels = ['',''])\n",
    "plt.yticks([36.027, 36.029, 36.031], labels = ['','',''])\n",
    "plt.xlim([129.291, 129.2955])\n",
    "plt.ylim([36.026, 36.032])\n",
    "\n",
    "\n",
    "plt.title(\"\")\n",
    "plt.xlabel(\"\")\n",
    "plt.ylabel('')\n",
    "\n",
    "plt.tick_params(length = 10) \n",
    "plt.grid(alpha=0.5, color='gray')\n",
    "\n",
    "plt.subplot(8,4,15)\n",
    "\n",
    "#for axis in ['top', 'bottom', 'left', 'right']:\n",
    "#  plt.gca().spines[axis].set_linewidth(3)\n",
    "\n",
    "plt.fill(x,y, color = 'gray')\n",
    "plt.plot(DJR[\"LON\"], DJR[\"LAT\"], \n",
    "         color='black', linewidth = 1)\n",
    "plt.scatter(df15_D[\"LON\"], df15_D[\"LAT\"], c=df15_D[\"Depth\"], cmap='rainbow_r',\n",
    "            vmin = 0, vmax =9, alpha=1, s=2, \n",
    "            marker = 's')\n",
    "\n",
    "plt.xticks([129.292, 129.294], \n",
    "           labels = ['',''])\n",
    "plt.yticks([36.027, 36.029, 36.031], labels = ['','',''])\n",
    "plt.xlim([129.291, 129.2955])\n",
    "plt.ylim([36.026, 36.032])\n",
    "\n",
    "plt.title(\"\")\n",
    "plt.xlabel(\"\")\n",
    "plt.ylabel('')\n",
    "plt.tick_params(length = 10) \n",
    "plt.grid(alpha=0.5, color='gray')\n",
    "\n",
    "\n",
    "plt.subplot(8,4,16)\n",
    "plt.fill(x,y, color = 'gray')\n",
    "plt.plot(DJR[\"LON\"], DJR[\"LAT\"], \n",
    "         color='black', linewidth = 1)\n",
    "plt.scatter(df16_D[\"LON\"], df16_D[\"LAT\"], c=df16_D[\"Depth\"], cmap='rainbow_r',\n",
    "            vmin = 0, vmax =9, alpha=1, s=2, \n",
    "            marker = 's')\n",
    "\n",
    "plt.xticks([129.292, 129.294], \n",
    "           labels = ['',''])\n",
    "plt.yticks([36.027, 36.029, 36.031], labels = ['','',''])\n",
    "plt.xlim([129.291, 129.2955])\n",
    "plt.ylim([36.026, 36.032])\n",
    "\n",
    "\n",
    "plt.title(\"\")\n",
    "plt.xlabel(\"\")\n",
    "plt.ylabel('')\n",
    "\n",
    "plt.tick_params(length = 10) \n",
    "plt.grid(alpha=0.5, color='gray')\n",
    "\n",
    "plt.subplot(8,4,17)\n",
    "plt.fill(x,y, color = 'gray')\n",
    "plt.plot(DJR[\"LON\"], DJR[\"LAT\"], \n",
    "         color='black', linewidth = 1)\n",
    "plt.scatter(df17_D[\"LON\"], df17_D[\"LAT\"], c=df17_D[\"Depth\"], cmap='rainbow_r',\n",
    "            vmin = 0, vmax =9, alpha=1, s=2, \n",
    "            marker = 's')\n",
    "\n",
    "\n",
    "plt.xticks([129.292, 129.294], \n",
    "           labels = ['',''])\n",
    "\n",
    "plt.xlim([129.291, 129.2955])\n",
    "plt.yticks([36.027, 36.029, 36.031], labels = ['','',''])\n",
    "plt.ylim([36.026, 36.032])\n",
    "\n",
    "plt.title(\"\")\n",
    "plt.xlabel(\"\")\n",
    "plt.ylabel('')\n",
    "\n",
    "plt.tick_params(length = 10) \n",
    "plt.grid(alpha=0.5, color='gray')\n",
    "\n",
    "plt.subplot(8,4,18)\n",
    "plt.fill(x,y, color = 'gray')\n",
    "plt.plot(DJR[\"LON\"], DJR[\"LAT\"], \n",
    "         color='black', linewidth = 1)\n",
    "plt.scatter(df18_D[\"LON\"], df18_D[\"LAT\"], c=df18_D[\"Depth\"], cmap='rainbow_r',\n",
    "            vmin = 0, vmax =9, alpha=1, s=2, \n",
    "            marker = 's')\n",
    "\n",
    "\n",
    "plt.yticks([36.027, 36.029, 36.031], labels = ['','',''])\n",
    "plt.xticks([129.292, 129.294], \n",
    "           labels = ['',''])\n",
    "plt.xlim([129.291, 129.2955])\n",
    "plt.ylim([36.026, 36.032])\n",
    "\n",
    "plt.title(\"\")\n",
    "plt.xlabel(\"\")\n",
    "plt.ylabel('')\n",
    "plt.tick_params(length = 10) \n",
    "plt.grid(alpha=0.5, color='gray')\n",
    "######################################################\n",
    "######################################################\n",
    "######################################################\n",
    "plt.subplot(8,4,19)\n",
    "x, y = fill_outside(DJR[\"LON\"], DJR[\"LAT\"], ll, ur)\n",
    "\n",
    "plt.fill(x,y, color = 'gray')\n",
    "plt.plot(DJR[\"LON\"], DJR[\"LAT\"], \n",
    "         color='black', linewidth = 1)\n",
    "plt.scatter(df19_D[\"LON\"], df19_D[\"LAT\"], \n",
    "            c=df19_D[\"Depth\"], cmap='rainbow_r',\n",
    "            vmin = 0, vmax =9, alpha=1, s=2, marker = 's')\n",
    "\n",
    "plt.xticks([129.292, 129.294], \n",
    "           labels = ['',''])\n",
    "\n",
    "plt.xlim([129.291, 129.2955])\n",
    "plt.yticks([36.027, 36.029, 36.031], labels = ['','',''])\n",
    "plt.ylim([36.026, 36.032])\n",
    "plt.title(\"\")\n",
    "plt.xlabel(\"\")\n",
    "plt.ylabel('')\n",
    "\n",
    "plt.tick_params(length = 10) \n",
    "plt.grid(alpha=0.5, color='gray')\n",
    "######################################################\n",
    "######################################################\n",
    "######################################################\n",
    "plt.subplot(8,4,20)\n",
    "x, y = fill_outside(DJR[\"LON\"], DJR[\"LAT\"], ll, ur)\n",
    "\n",
    "plt.fill(x,y, color = 'gray')\n",
    "plt.plot(DJR[\"LON\"], DJR[\"LAT\"], \n",
    "         color='black', linewidth = 1)\n",
    "plt.scatter(df20_D[\"LON\"], df20_D[\"LAT\"], \n",
    "            c=df20_D[\"Depth\"], cmap='rainbow_r',\n",
    "            vmin = 0, vmax =9, alpha=1, s=2, marker = 's')\n",
    "\n",
    "plt.xticks([129.292, 129.294], \n",
    "           labels = ['',''])\n",
    "\n",
    "plt.xlim([129.291, 129.2955])\n",
    "plt.yticks([36.027, 36.029, 36.031], labels = ['','',''])\n",
    "plt.ylim([36.026, 36.032])\n",
    "plt.title(\"\")\n",
    "plt.xlabel(\"\")\n",
    "plt.ylabel('')\n",
    "\n",
    "plt.tick_params(length = 10) \n",
    "plt.grid(alpha=0.5, color='gray')\n",
    "######################################################\n",
    "######################################################\n",
    "######################################################\n",
    "plt.subplot(8,4,21)\n",
    "x, y = fill_outside(DJR[\"LON\"], DJR[\"LAT\"], ll, ur)\n",
    "\n",
    "plt.fill(x,y, color = 'gray')\n",
    "plt.plot(DJR[\"LON\"], DJR[\"LAT\"], \n",
    "         color='black', linewidth = 1)\n",
    "plt.scatter(df21_D[\"LON\"], df21_D[\"LAT\"], \n",
    "            c=df21_D[\"Depth\"], cmap='rainbow_r',\n",
    "            vmin = 0, vmax =9, alpha=1, s=2, marker = 's')\n",
    "\n",
    "plt.xticks([129.292, 129.294], \n",
    "           labels = ['',''])\n",
    "\n",
    "plt.xlim([129.291, 129.2955])\n",
    "plt.yticks([36.027, 36.029, 36.031], labels = ['','',''])\n",
    "plt.ylim([36.026, 36.032])\n",
    "plt.title(\"\")\n",
    "plt.xlabel(\"\")\n",
    "plt.ylabel('')\n",
    "\n",
    "plt.tick_params(length = 10) \n",
    "plt.grid(alpha=0.5, color='gray')\n",
    "\n",
    "######################################################\n",
    "######################################################\n",
    "######################################################\n",
    "plt.subplot(8,4,22)\n",
    "x, y = fill_outside(DJR[\"LON\"], DJR[\"LAT\"], ll, ur)\n",
    "\n",
    "plt.fill(x,y, color = 'gray')\n",
    "plt.plot(DJR[\"LON\"], DJR[\"LAT\"], \n",
    "         color='black', linewidth = 1)\n",
    "plt.scatter(df22_D[\"LON\"], df22_D[\"LAT\"], \n",
    "            c=df22_D[\"Depth\"], cmap='rainbow_r',\n",
    "            vmin = 0, vmax =9, alpha=1, s=2, marker = 's')\n",
    "\n",
    "plt.xticks([129.292, 129.294], \n",
    "           labels = ['',''])\n",
    "\n",
    "plt.xlim([129.291, 129.2955])\n",
    "plt.yticks([36.027, 36.029, 36.031], labels = ['','',''])\n",
    "plt.ylim([36.026, 36.032])\n",
    "plt.title(\"\")\n",
    "plt.xlabel(\"\")\n",
    "plt.ylabel('')\n",
    "\n",
    "plt.tick_params(length = 10) \n",
    "plt.grid(alpha=0.5, color='gray')\n",
    "######################################################\n",
    "######################################################\n",
    "######################################################\n",
    "plt.subplot(8,4,23)\n",
    "x, y = fill_outside(DJR[\"LON\"], DJR[\"LAT\"], ll, ur)\n",
    "\n",
    "plt.fill(x,y, color = 'gray')\n",
    "plt.plot(DJR[\"LON\"], DJR[\"LAT\"], \n",
    "         color='black', linewidth = 1)\n",
    "plt.scatter(df23_D[\"LON\"], df23_D[\"LAT\"], \n",
    "            c=df23_D[\"Depth\"], cmap='rainbow_r',\n",
    "            vmin = 0, vmax =9, alpha=1, s=2, marker = 's')\n",
    "\n",
    "plt.xticks([129.292, 129.294], \n",
    "           labels = ['',''])\n",
    "\n",
    "plt.xlim([129.291, 129.2955])\n",
    "plt.yticks([36.027, 36.029, 36.031], labels = ['','',''])\n",
    "plt.ylim([36.026, 36.032])\n",
    "plt.title(\"\")\n",
    "plt.xlabel(\"\")\n",
    "plt.ylabel('')\n",
    "\n",
    "plt.tick_params(length = 10) \n",
    "plt.grid(alpha=0.5, color='gray')\n",
    "\n",
    "######################################################\n",
    "######################################################\n",
    "######################################################\n",
    "plt.subplot(8,4,24)\n",
    "x, y = fill_outside(DJR[\"LON\"], DJR[\"LAT\"], ll, ur)\n",
    "\n",
    "plt.fill(x,y, color = 'gray')\n",
    "plt.plot(DJR[\"LON\"], DJR[\"LAT\"], \n",
    "         color='black', linewidth = 1)\n",
    "plt.scatter(df24_D[\"LON\"], df24_D[\"LAT\"], \n",
    "            c=df24_D[\"Depth\"], cmap='rainbow_r',\n",
    "            vmin = 0, vmax =9, alpha=1, s=2, marker = 's')\n",
    "\n",
    "plt.xticks([129.292, 129.294], \n",
    "           labels = ['',''])\n",
    "\n",
    "plt.xlim([129.291, 129.2955])\n",
    "plt.yticks([36.027, 36.029, 36.031], labels = ['','',''])\n",
    "plt.ylim([36.026, 36.032])\n",
    "plt.title(\"\")\n",
    "plt.xlabel(\"\")\n",
    "plt.ylabel('')\n",
    "\n",
    "plt.tick_params(length = 10) \n",
    "plt.grid(alpha=0.5, color='gray')\n",
    "######################################################\n",
    "######################################################\n",
    "######################################################\n",
    "plt.subplot(8,4,25)\n",
    "x, y = fill_outside(DJR[\"LON\"], DJR[\"LAT\"], ll, ur)\n",
    "\n",
    "plt.fill(x,y, color = 'gray')\n",
    "plt.plot(DJR[\"LON\"], DJR[\"LAT\"], \n",
    "         color='black', linewidth = 1)\n",
    "plt.scatter(df25_D[\"LON\"], df25_D[\"LAT\"], \n",
    "            c=df25_D[\"Depth\"], cmap='rainbow_r',\n",
    "            vmin = 0, vmax =9, alpha=1, s=2, marker = 's')\n",
    "\n",
    "plt.xticks([129.292, 129.294], \n",
    "           labels = ['',''])\n",
    "\n",
    "plt.xlim([129.291, 129.2955])\n",
    "plt.yticks([36.027, 36.029, 36.031], labels = ['','',''])\n",
    "plt.ylim([36.026, 36.032])\n",
    "plt.title(\"\")\n",
    "plt.xlabel(\"\")\n",
    "plt.ylabel('')\n",
    "\n",
    "plt.tick_params(length = 10) \n",
    "plt.grid(alpha=0.5, color='gray')\n",
    "\n",
    "######################################################\n",
    "######################################################\n",
    "######################################################\n",
    "plt.subplot(8,4,26)\n",
    "x, y = fill_outside(DJR[\"LON\"], DJR[\"LAT\"], ll, ur)\n",
    "\n",
    "plt.fill(x,y, color = 'gray')\n",
    "plt.plot(DJR[\"LON\"], DJR[\"LAT\"], \n",
    "         color='black', linewidth = 1)\n",
    "plt.scatter(df26_D[\"LON\"], df26_D[\"LAT\"], \n",
    "            c=df26_D[\"Depth\"], cmap='rainbow_r',\n",
    "            vmin = 0, vmax =9, alpha=1, s=2, marker = 's')\n",
    "\n",
    "plt.xticks([129.292, 129.294], \n",
    "           labels = ['',''])\n",
    "\n",
    "plt.xlim([129.291, 129.2955])\n",
    "plt.yticks([36.027, 36.029, 36.031], labels = ['','',''])\n",
    "plt.ylim([36.026, 36.032])\n",
    "plt.title(\"\")\n",
    "plt.xlabel(\"\")\n",
    "plt.ylabel('')\n",
    "\n",
    "plt.tick_params(length = 10) \n",
    "plt.grid(alpha=0.5, color='gray')\n",
    "\n",
    "######################################################\n",
    "######################################################\n",
    "######################################################\n",
    "plt.subplot(8,4,27)\n",
    "x, y = fill_outside(DJR[\"LON\"], DJR[\"LAT\"], ll, ur)\n",
    "\n",
    "plt.fill(x,y, color = 'gray')\n",
    "plt.plot(DJR[\"LON\"], DJR[\"LAT\"], \n",
    "         color='black', linewidth = 1)\n",
    "plt.scatter(df27_D[\"LON\"], df27_D[\"LAT\"], \n",
    "            c=df27_D[\"Depth\"], cmap='rainbow_r',\n",
    "            vmin = 0, vmax =9, alpha=1, s=2, marker = 's')\n",
    "\n",
    "plt.xticks([129.292, 129.294], \n",
    "           labels = ['',''])\n",
    "\n",
    "plt.xlim([129.291, 129.2955])\n",
    "plt.yticks([36.027, 36.029, 36.031], labels = ['','',''])\n",
    "plt.ylim([36.026, 36.032])\n",
    "plt.title(\"\")\n",
    "plt.xlabel(\"\")\n",
    "plt.ylabel('')\n",
    "\n",
    "plt.tick_params(length = 10) \n",
    "plt.grid(alpha=0.5, color='gray') \n",
    "######################################################\n",
    "######################################################\n",
    "######################################################\n",
    "plt.subplot(8,4,28)\n",
    "x, y = fill_outside(DJR[\"LON\"], DJR[\"LAT\"], ll, ur)\n",
    "\n",
    "plt.fill(x,y, color = 'gray')\n",
    "plt.plot(DJR[\"LON\"], DJR[\"LAT\"], \n",
    "         color='black', linewidth = 1)\n",
    "plt.scatter(df28_D[\"LON\"], df28_D[\"LAT\"], \n",
    "            c=df28_D[\"Depth\"], cmap='rainbow_r',\n",
    "            vmin = 0, vmax =9, alpha=1, s=2, marker = 's')\n",
    "\n",
    "plt.xticks([129.292, 129.294], \n",
    "           labels = ['',''])\n",
    "\n",
    "plt.xlim([129.291, 129.2955])\n",
    "plt.yticks([36.027, 36.029, 36.031], labels = ['','',''])\n",
    "plt.ylim([36.026, 36.032])\n",
    "plt.title(\"\")\n",
    "plt.xlabel(\"\")\n",
    "plt.ylabel('')\n",
    "\n",
    "plt.tick_params(length = 10) \n",
    "plt.grid(alpha=0.5, color='gray')\n",
    "######################################################\n",
    "######################################################\n",
    "######################################################\n",
    "plt.subplot(8,4,29)\n",
    "x, y = fill_outside(DJR[\"LON\"], DJR[\"LAT\"], ll, ur)\n",
    "\n",
    "plt.fill(x,y, color = 'gray')\n",
    "plt.plot(DJR[\"LON\"], DJR[\"LAT\"], \n",
    "         color='black', linewidth = 1)\n",
    "plt.scatter(df29_D[\"LON\"], df29_D[\"LAT\"], \n",
    "            c=df29_D[\"Depth\"], cmap='rainbow_r',\n",
    "            vmin = 0, vmax =9, alpha=1, s=2, marker = 's')\n",
    "\n",
    "plt.xticks([129.292, 129.294], \n",
    "           labels = ['',''])\n",
    "\n",
    "plt.xlim([129.291, 129.2955])\n",
    "plt.yticks([36.027, 36.029, 36.031], labels = ['','',''])\n",
    "plt.ylim([36.026, 36.032])\n",
    "plt.title(\"\")\n",
    "plt.xlabel(\"\")\n",
    "plt.ylabel('')\n",
    "\n",
    "plt.tick_params(length = 10) \n",
    "plt.grid(alpha=0.5, color='gray')\n",
    "\n",
    "######################################################\n",
    "######################################################\n",
    "######################################################\n",
    "plt.subplot(8,4,30)\n",
    "x, y = fill_outside(DJR[\"LON\"], DJR[\"LAT\"], ll, ur)\n",
    "\n",
    "plt.fill(x,y, color = 'gray')\n",
    "plt.plot(DJR[\"LON\"], DJR[\"LAT\"], \n",
    "         color='black', linewidth = 1)\n",
    "plt.scatter(df30_D[\"LON\"], df30_D[\"LAT\"], \n",
    "            c=df30_D[\"Depth\"], cmap='rainbow_r',\n",
    "            vmin = 0, vmax =9, alpha=1, s=2, marker = 's')\n",
    "\n",
    "plt.xticks([129.292, 129.294], \n",
    "           labels = ['',''])\n",
    "\n",
    "plt.xlim([129.291, 129.2955])\n",
    "plt.yticks([36.027, 36.029, 36.031], labels = ['','',''])\n",
    "plt.ylim([36.026, 36.032])\n",
    "plt.title(\"\")\n",
    "plt.xlabel(\"\")\n",
    "plt.ylabel('')\n",
    "\n",
    "plt.tick_params(length = 10) \n",
    "plt.grid(alpha=0.5, color='gray')\n",
    "######################################################\n",
    "######################################################\n",
    "######################################################\n",
    "plt.subplot(8,4,31)\n",
    "x, y = fill_outside(DJR[\"LON\"], DJR[\"LAT\"], ll, ur)\n",
    "\n",
    "plt.fill(x,y, color = 'gray')\n",
    "plt.plot(DJR[\"LON\"], DJR[\"LAT\"], \n",
    "         color='black', linewidth = 1)\n",
    "plt.scatter(df31_D[\"LON\"], df31_D[\"LAT\"], \n",
    "            c=df31_D[\"Depth\"], cmap='rainbow_r',\n",
    "            vmin = 0, vmax =9, alpha=1, s=2, marker = 's')\n",
    "\n",
    "plt.xticks([129.292, 129.294], \n",
    "           labels = ['',''])\n",
    "\n",
    "plt.xlim([129.291, 129.2955])\n",
    "plt.yticks([36.027, 36.029, 36.031], labels = ['','',''])\n",
    "plt.ylim([36.026, 36.032])\n",
    "plt.title(\"\")\n",
    "plt.xlabel(\"\")\n",
    "plt.ylabel('')\n",
    "\n",
    "plt.tick_params(length = 10) \n",
    "plt.grid(alpha=0.5, color='gray')\n",
    "######################################################\n",
    "######################################################\n",
    "######################################################\n",
    "plt.subplot(8,4,32)\n",
    "x, y = fill_outside(DJR[\"LON\"], DJR[\"LAT\"], ll, ur)\n",
    "\n",
    "plt.fill(x,y, color = 'gray')\n",
    "plt.plot(DJR[\"LON\"], DJR[\"LAT\"], \n",
    "         color='black', linewidth = 1)\n",
    "plt.scatter(df32_D[\"LON\"], df32_D[\"LAT\"], \n",
    "            c=df32_D[\"Depth\"], cmap='rainbow_r',\n",
    "            vmin = 0, vmax =9, alpha=1, s=2, marker = 's')\n",
    "\n",
    "plt.xticks([129.292, 129.294], \n",
    "           labels = ['',''])\n",
    "\n",
    "plt.xlim([129.291, 129.2955])\n",
    "plt.yticks([36.027, 36.029, 36.031], labels = ['','',''])\n",
    "plt.ylim([36.026, 36.032])\n",
    "plt.title(\"\")\n",
    "plt.xlabel(\"\")\n",
    "plt.ylabel('')\n",
    "\n",
    "plt.tick_params(length = 10) \n",
    "plt.grid(alpha=0.5, color='gray')\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "plt.savefig('Figure 5.png',bbox_inches = \"tight\", dpi = 600)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "4d7d8e4c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 79.2x79.2 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig,ax = plt.subplots(figsize=(1.1,1.1))\n",
    "\n",
    "cax = ax.scatter(df10[\"LON\"], df10[\"LAT\"], c=df10[\"Depth\"], cmap='rainbow_r',vmin = 0, vmax =9, alpha=.6, s=0.1)\n",
    "\n",
    "plt.xticks([129.292, 129.294], \n",
    "           labels = ['',''])\n",
    "\n",
    "plt.xlim([129.291, 129.2955])\n",
    "plt.yticks([36.027, 36.029, 36.031], labels = ['','',''])\n",
    "plt.ylim([36.026, 36.032])\n",
    "plt.grid()\n",
    "\n",
    "cbar = fig.colorbar(cax, ticks=[0,3,6,9])\n",
    "cbar.ax.set_yticklabels(['','', '', ''])\n",
    "cbar.ax.yaxis.set_tick_params(length = 10)\n",
    "\n",
    "plt.tick_params(length = 10) \n",
    "plt.title(\"\")\n",
    "plt.xlabel(\"\")\n",
    "plt.ylabel('')\n",
    "plt.savefig('Figure 5 colorbar.png',bbox_inches = \"tight\", dpi = 600)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "bf71efe6",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
